The inefficiency of literature searches is an old informatics problem. There has been much progress in facilitating literature searches, such as the registration of trials and observational studies, the proposal of optimal search strategies for various fields, and the proposal of search filters, but the efficiency of literature searches has not markedly improved. The goal of this project is to strengthen the scientific basis of the CoCites method, a novel citation-based search method for scientific literature that we recently developed. The method aims to find articles that are similar to one or more `known' articles in two consecutive steps by: 1) ranking articles that are cited together with the known articles on their co-citation frequencies, and 2) ranking backward and forward citations on their citation frequencies. Articles with frequencies above the selection thresholds are screened for similarity. In two pilot studies, where we aimed to reproduce the literature searches of 52 published meta-analyses, we showed that articles included in each meta-analysis ranked high on the list of (co-)citation frequencies. The CoCites method was more efficient than keyword searches and able to retrieve 80% of the studies included in the meta-analyses. The observed sensitivity of 80% represents the number of retrieved articles by the CoCites method as compared to the multiple resources that were utilized in the published meta-analyses. As recommended, authors search additional resources to retrieve studies that may be missed in the primary Medline or EMBASE databases, such as dissertations, grey literature and publications in non-English languages such as indexed in LILACS (South American) and KoreaMed (Asian) databases. Such resources can also be used to supplement the CoCites method. Therefore, in this project, we aim to compare the sensitivity and efficiency of the CoCites method to searches in MEDLINE and Embase only, to investigate whether and when the CoCites method could be an efficient first method to use in literature searches. Similar as in the pilot studies, we will reproduce the literature searches of published meta-analyses, which allows to calculate the sensitivity and efficiency of the method. We will focus on Cochrane systematic reviews as their appendices report the exact search strategies that were used in MEDLINE and Embase. For 50 published Cochrane reviews, we will compare three searches: 1) MEDLINE; 2) Embase; and 3) our CoCites method in Web of Science. We will record the number of articles that needs to be screened as well as the number of studies from the meta-analysis that is retrieved, and calculate sensitivity and efficiency. Finally, we perform a qualitative analysis of differences in retrieved studies to gain insight in the potential limitations of each method.
Finding eligible studies for meta-analysis and systematic reviews relies on keyword-based searching as the gold standard, despite its inefficiency. This project investigates a novel search method that ranks articles on their degree of co-citation with one or more known articles before reviewing their eligibility. This method, applied over time, will strengthen the connections between articles, promote the accumulation of evidence, and facilitate evidence synthesis.
